Scott Lathrop

Bio: Scott Lathrop is an academic researcher from University of Illinois at Urbana–Champaign. The author has contributed to research in topics: Cyberinfrastructure & Workforce. The author has an hindex of 6, co-authored 12 publications receiving 2293 citations.

XSEDE: Accelerating Scientific Discovery

Abstract: Computing in science and engineering is now ubiquitous: digital technologies underpin, accelerate, and enable new, even transformational, research in all domains. Access to an array of integrated and well-supported high-end digital services is critical for the advancement of knowledge. Driven by community needs, the Extreme Science and Engineering Discovery Environment (XSEDE) project substantially enhances the productivity of a growing community of scholars, researchers, and engineers (collectively referred to as "scientists"' throughout this article) through access to advanced digital services that support open research. XSEDE's integrated, comprehensive suite of advanced digital services federates with other high-end facilities and with campus-based resources, serving as the foundation for a national e-science infrastructure ecosystem. XSEDE's e-science infrastructure has tremendous potential for enabling new advancements in research and education. XSEDE's vision is a world of digitally enabled scholars, researchers, and engineers participating in multidisciplinary collaborations to tackle society's grand challenges.

High-Performance Computing Education

Abstract: Many successful efforts are currently addressing the critical shortage of a diverse, well-prepared high-performance computing (HPC) workforce. The guest editors' goal, with this issue, is to stimulate large-scale international discourse to accelerate the adoption of the educational tools, curriculum, and pedagogy that reflect the increasing role of computational methods in science and engineering. Such approaches are necessary to educate a larger and more diverse population of well-skilled, knowledgeable, and innovative people who will significantly advance scientific discovery in all fields of study, now and well into the future.

A Call to Action to Prepare the High-Performance Computing Workforce

Abstract: This article presents the challenges and opportunities for preparing current and future generations to advance research, scholarship, and education with high-performance computing

Incorporating Scientific Workflows in Computing Research Processes

Abstract: & WE ARE PLEASED to present this special issue on “Incorporating Scientific Workflows in Computing Research Processes” with a goal of realizing a significant increase in the development and usage of high-quality workflow tools, environments, and methods in the scientific community. This special issue aspires to increase awareness of the benefits of workflows to enhance computational and data-enabled research and to foster the exchange of lessons learned and good practices that can benefit the community. The issue highlights some of the activities and approaches that are underway in the scientific workflow community. Scientific workflows evolved as a way to manage computation onHigh Performance Computing (HPC) and distributed systems. Early workflow efforts started as domain-specific efforts that managed directed acyclic graphs on high-performance and distributed systems. They considered the systems and the applications as black boxes and focused on distributed resource management, workload, and execution management. In the mid-2000s, there were efforts to provide taxonomiesm, classifications for workflow systems, and a perspective of the field. The papers included in this special issue constitute a subset of the many workflow challenges and systems that exist in the community, but nonetheless represent the breadth of the systems and capabilities that are available. The special issue also spans a swath of time—from tools that have been around for multiple decades as well as more recent innovations. The six papers selected for the special issue cover a range of topics in workflows and workflow systems, and can be classified into three broad areas: 1) a description of specific workflow tools; 2) experience and investigation into social and technical aspects of Digital Object Identifier 10.1109/MCSE.2019.2917987

The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2018 update

Abstract: Galaxy (homepage: https://galaxyproject.org, main public server: https://usegalaxy.org) is a web-based scientific analysis platform used by tens of thousands of scientists across the world to analyze large biomedical datasets such as those found in genomics, proteomics, metabolomics and imaging. Started in 2005, Galaxy continues to focus on three key challenges of data-driven biomedical science: making analyses accessible to all researchers, ensuring analyses are completely reproducible, and making it simple to communicate analyses so that they can be reused and extended. During the last two years, the Galaxy team and the open-source community around Galaxy have made substantial improvements to Galaxy's core framework, user interface, tools, and training materials. Framework and user interface improvements now enable Galaxy to be used for analyzing tens of thousands of datasets, and >5500 tools are now available from the Galaxy ToolShed. The Galaxy community has led an effort to create numerous high-quality tutorials focused on common types of genomic analyses. The Galaxy developer and user communities continue to grow and be integral to Galaxy's development. The number of Galaxy public servers, developers contributing to the Galaxy framework and its tools, and users of the main Galaxy server have all increased substantially.

iDEP: an integrated web application for differential expression and pathway analysis of RNA-Seq data

Abstract: RNA-seq is widely used for transcriptomic profiling, but the bioinformatics analysis of resultant data can be time-consuming and challenging, especially for biologists. We aim to streamline the bioinformatic analyses of gene-level data by developing a user-friendly, interactive web application for exploratory data analysis, differential expression, and pathway analysis. iDEP (integrated Differential Expression and Pathway analysis) seamlessly connects 63 R/Bioconductor packages, 2 web services, and comprehensive annotation and pathway databases for 220 plant and animal species. The workflow can be reproduced by downloading customized R code and related pathway files. As an example, we analyzed an RNA-Seq dataset of lung fibroblasts with Hoxa1 knockdown and revealed the possible roles of SP1 and E2F1 and their target genes, including microRNAs, in blocking G1/S transition. In another example, our analysis shows that in mouse B cells without functional p53, ionizing radiation activates the MYC pathway and its downstream genes involved in cell proliferation, ribosome biogenesis, and non-coding RNA metabolism. In wildtype B cells, radiation induces p53-mediated apoptosis and DNA repair while suppressing the target genes of MYC and E2F1, and leads to growth and cell cycle arrest. iDEP helps unveil the multifaceted functions of p53 and the possible involvement of several microRNAs such as miR-92a, miR-504, and miR-30a. In both examples, we validated known molecular pathways and generated novel, testable hypotheses. Combining comprehensive analytic functionalities with massive annotation databases, iDEP ( http://ge-lab.org/idep/ ) enables biologists to easily translate transcriptomic and proteomic data into actionable insights.

Mechanical Properties of Organic Semiconductors for Stretchable, Highly Flexible, and Mechanically Robust Electronics

Abstract: Mechanical deformability underpins many of the advantages of organic semiconductors. The mechanical properties of these materials are, however, diverse, and the molecular characteristics that permit charge transport can render the materials stiff and brittle. This review is a comprehensive description of the molecular and morphological parameters that govern the mechanical properties of organic semiconductors. Particular attention is paid to ways in which mechanical deformability and electronic performance can coexist. The review begins with a discussion of flexible and stretchable devices of all types, and in particular the unique characteristics of organic semiconductors. It then discusses the mechanical properties most relevant to deformable devices. In particular, it describes how low modulus, good adhesion, and absolute extensibility prior to fracture enable robust performance, along with mechanical “imperceptibility” if worn on the skin. A description of techniques of metrology precedes a discussion...

Ruthenium atomically dispersed in carbon outperforms platinum toward hydrogen evolution in alkaline media.

Abstract: Hydrogen evolution reaction is an important process in electrochemical energy technologies. Herein, ruthenium and nitrogen codoped carbon nanowires are prepared as effective hydrogen evolution catalysts. The catalytic performance is markedly better than that of commercial platinum catalyst, with an overpotential of only -12 mV to reach the current density of 10 mV cm-2 in 1 M KOH and -47 mV in 0.1 M KOH. Comparisons with control experiments suggest that the remarkable activity is mainly ascribed to individual ruthenium atoms embedded within the carbon matrix, with minimal contributions from ruthenium nanoparticles. Consistent results are obtained in first-principles calculations, where RuCxNy moieties are found to show a much lower hydrogen binding energy than ruthenium nanoparticles, and a lower kinetic barrier for water dissociation than platinum. Among these, RuC2N2 stands out as the most active catalytic center, where both ruthenium and adjacent carbon atoms are the possible active sites.

COFACTOR: improved protein function prediction by combining structure, sequence and protein–protein interaction information

Abstract: The COFACTOR web server is a unified platform for structure-based multiple-level protein function predictions. By structurally threading low-resolution structural models through the BioLiP library, the COFACTOR server infers three categories of protein functions including gene ontology, enzyme commission and ligand-binding sites from various analogous and homologous function templates. Here, we report recent improvements of the COFACTOR server in the development of new pipelines to infer functional insights from sequence profile alignments and protein-protein interaction networks. Large-scale benchmark tests show that the new hybrid COFACTOR approach significantly improves the function annotation accuracy of the former structure-based pipeline and other state-of-the-art functional annotation methods, particularly for targets that have no close homology templates. The updated COFACTOR server and the template libraries are available at http://zhanglab.ccmb.med.umich.edu/COFACTOR/.